Working capital policy of newly incorporated firms

Abstract

Purpose

The present study examines the initial working capital policy (WCP) and its evolution for newly established manufacturing firms.

Design/methodology/approach

Using panel data of 162 firms over a period of 10 years, the study analyses the persistence-cum-convergence in WCP over the subsequent years through descriptive analysis and difference of means test. Further, the prevalence of ß – convergence, and σ-convergence has been examined using standard least squares regression, dynamic panel analysis and the Wald test.

Findings

The results indicate that sample firms continue to follow the initial WCP in the subsequent years with a gradual convergence in the WCP. Alternatively, the firms with aggressive (conservative) WCP at the time of incorporation will continue following it. Further, the firms with aggressive initial WCP have witnessed higher growth than those with conservative initial WCP.

Research limitations/implications

Findings will assist managers and practitioners to understand the dynamics of WCP over the life cycle of the firm and select appropriate WCP as certain policies lead to certain growth paths.

Originality/value

Though working capital management has been recognized as a critical managerial decision, limited research is available on its evolution, especially for newly established manufacturing companies in an emerging economy. Current research attempts to fill this gap and provide valuable insights for the effective management of liquidity.

Citation

Desai, R. and Mehta, B. (2024), "Working capital policy of newly incorporated firms", Asian Journal of Accounting Research, Vol. 9 No. 1, pp. 13-24. https://doi.org/10.1108/AJAR-02-2022-0066

1. Introduction

Working capital management, especially in a manufacturing firm, has been considered as one of the crucial business decisions (Fazzari et al., 2000) as it is a function of product mix, length of the operating cycle, activity level and credit terms (Bhalla, 2014). Among various finance decisions, the working capital decision stands quite different as it requires continuous monitoring and alterations based on the changes in the business environment. The decisions taken by firms concerning working capital lead to the adoption of a specific type of working capital policy (WCP), which is further sub-grouped as working capital investment policy (WCIP) and working capital financing policy (WCFP) (Bhalla, 2014). Provided the external dynamics, continuous evaluation of WCP is needed for retention or alteration and, in either type of decision, the proportion and pace of retention or alteration need to be studied. Extant literature has studied working capital as one of the determinants of profitability (Nam and Uchida, 2019; Morshed, 2020), considering it a static phenomenon. However, it is essential to study the evolution path of WCP as the adoption of specific WCP affects the financial performance of companies (Dary and James, 2019). Few studies such as Aktas et al. (2015) and Baños-Caballero et al. (2010) have examined the dynamic nature of working capital and suggested that firms can modulate WCP to match the maturities of assets and liabilities and consequently optimize their profitability. However, there is a dearth of conclusive research in this domain, especially in emerging economies. Therefore, the current study attempts to fill this gap and contributes to the existing literature.

The present research is aimed to study the evolution of WCP of manufacturing companies in the Indian context using panel data of 162 firms collected over a period of 10 years. Based on the statistical results using t-test and panel regression, the study concludes that firms with higher (lower) initial working capital ratios experience greater reduction (increase) in the same over the study period. However, the results also conclude that the initial difference in working capital ratios still persists during the study period as firms continue to pursue the same approach to managing liquidity as adopted at the time of incorporation. The present study has several contributions to the existing pool of working capital research. First, to the best of the authors' knowledge, this is the first study that uses the concept of β-convergence and σ-convergence from economic theory to explain working capital dynamics. Second, the study is based on newly incorporated firms that play a pivotal role in the economic development of emerging economies like India by driving economic growth and wealth creation through employment generation (Protogerou et al., 2017). Hence, it is worth finding the strategy adopted by these firms for liquidity management. Third, findings will assist corporate managers to devise their liquidity policy by examining the effect of initial WCP and other firm-specific determinants on the management of current assets and liabilities.

The remainder of the manuscript has been organized as follows. Section 2 highlights the review of the literature and the development of the hypothesis. Section 3 summarizes the research methods, followed by empirical results presented in section 4. Section 5 presents the impact of WCP on the firm's growth. Lastly, discussion, and implications and conclusion have been included in sections 6 and 7, respectively.

2. Review of literature

2.1 Theoretical literature

Conventionally, corporate finance functions are divided into four major categories, investment, financing, working capital and dividend (Chandra, 2008). Among these, the working capital decision follows a distinct set of principles as it is a recurring decision. Next, working capital primarily involves a trade-off between profitability and liquidity. In other words, though current assets generate lower returns compared to fixed assets, however, they can be easily converted into cash and hence lower liquidity risk. WCP can be further analysed through studying WCIP and WCFP (Ahmad et al., 2022). WCIP (WCFP) describes the levels and types of current assets (current liabilities) of a firm. Depending on risk appetite, firms adopt aggressive, conservative or moderate WCP (Bei and Wijewardana, 2012). Current assets generate a lower return compared to fixed assets, but the investment in current assets is for the short term, and therefore it also has lower liquidity risks (Mittal and Garg, 2022). The fixed obligation of short-term debt finance is less as compared to long-term debt finance, but the liquidity risk is higher due to its duration (Khan et al., 2022). Aggressive (conservative) WCIP indicates a lower (higher) proportion of current assets compared to total assets, whereas aggressive (conservative) WCFP exists when the proportion of current liabilities is higher (lower) than total liabilities (Moncef Guizani, 2022). In aggregate, the firm with a higher (lower) proportion of current assets and a lower (higher) proportion of current liabilities tends to have conservative (aggressive) WCP.

WCP of firms, in general, is considered a short-term decision, and therefore the frequency of this decision is believed to be more as compared to fixed assets investment and capital structure decisions (Bhalla, 2014). Considering this theoretical proposition, WCP is always subject to change and is independent of initial WCP. Further, firms' ability to alter their WCP depends largely upon the freedom to take operating decisions such as a policy of trade credit, inventory, suppliers and banking (Cho et al., 2019). This freedom to make decisions depends upon the firm's bargaining power with its stakeholders. Firms with higher (lower) bargaining power are able (unable) to take desirable actions and require less (more) modulations in their WCP (Clarkson et al., 2020). Further, higher (lower) bargaining power allows (does not allow) firms to follow their initial WCP in the subsequent years (Fontaine and Zhao, 2021). Figure 1 (available online at: https://drive.google.com/drive/folders/1NpOVvfjKX2cTrMFWrr_Mz-Zozgh6tOLL?usp=sharing) explains these probabilities.

2.2 Empirical literature

Working capital is one of the highly researched topics under the corporate finance and conducted on varied themes. It has carried out on an aggregate basis through the study of gross assets or net working capital (Aktas et al., 2015; Chauhan, 2019). The research has been also done focusing on of the component of working capital such as cash (Chang et al., 2019), receivables (Grau and Reig, 2018), payables (Nam and Uchida, 2019) and inventory (Dbouk et al., 2020). But no literature has analysed the concept at both aggregate and component levels simultaneously. Besides accounting ratios, working capital has also been examined using the operating cycle of firms. This cycle depends on the firm's business structure that usually remains similar within industry. Another developed stream of working capital research is the nexus between firm's performance and liquidity as studied by existing researchers such as Nam and Uchida (2019), Dary and James (2019) and Kasiran et al. (2016). Sawarni et al. (2021) measured the firm's performance by taking Tobin's Q and return on equity and found that WCP had an impact on the firm's performance. Rey-Ares et al. (2021) have used return on assets and return on equity to measure the economic and financial performance impact on Spanish fish canning companies with reference to working capital. It found that both measures had an impact on the collection and inventory period. It is found from the existing literature that the ROA and ROE have been majorly considered to measure the firm's growth ,and WCP does have impact on firm's performance. But again, the relation of WCP and growth of new firms is unexplored.

Working capital is a multidimensional concept, and hence the prevailing literature has explored the type of WCP adopted by firms under different circumstances. Jabbouri et al. (2022) have studied the type of WCP adopted by firms and found that the financially constrained firms have aggressive WCP by less investment in current assets. Similar results have been generated by Altaf and Shah (2017), Banerjee et al. (2021) and Dhole et al. (2019) with reference to financial constraints and WCP for the different macro environments. Thus, it is worth understanding the WCP adopted by the newly incorporated firms over the years owing to its market constraints. Several past studies have examined the determinants of WCP such as ownership structure (Dary and James, 2019; Mortal et al., 2020), industry (Grau and Reig, 2018) and firm size (Kasiran et al., 2016). Anton and Nucu (2022) have studied accounting as well as governance factors affecting liquidity of European listed firms. They have concluded that strong institutional framework follow aggressive WCP. Besides the peripheral factors as indicated earlier, firms' existing WCP may be an outcome of policies adopted at the time of establishment as the founder/promoter characteristics affect the decision theory of the organization. Though working capital has been well established in literature, the association between initial WCP and the existing one has remained underexplored. This may give rise to two mutually exclusive possibilities hypothesized as follows.

H1.

Differences in initial WCP will gradually disappear, and working capital ratios converge over time.

H2.

Differences in initial WCP will not disappear, and working capital ratios persist over time.

2.3 Research gap

Review discussion portrays working capital as one of the highly researched areas consisting both breadth and depth. However, there are few gaps which necessitate further probing. First, past studies are conducted on mature and developed firms, and limited evidence is available for newly incorporated firms. Newly incorporated firms play a pivotal role in the economic development of emerging economies, like India. These firms drive economic growth and wealth creation by providing employment opportunities (Protogerou et al., 2017). The approach of these firms towards various business decisions can be studied to understand the market and industrial practices (Hirsch and Walz, 2019). Hence, it is worth finding the strategy adopted by newly incorporated firms towards WCP over the subsequent years of inception. Second, the effect of initial WCP on the liquidity decision requires further investigation as well as its impact on firm's growth with reference to emerging economies. To sum up, present study attempts to fill these gaps and add value to the growing field of corporate finance research.

3. Research methodology

3.1 Variables of the study

Table 1 (available online at: https://drive.google.com/drive/folders/1NpOVvfjKX2cTrMFWrr_Mz-Zozgh6tOLL?usp=sharing) operationalizes the variables of the study along with their computation. Besides working capital, other financial characteristics are also considered as control variables to improve reliability of results (Moussa, 2019).

3.2 Data and sample

Manufacturing companies incorporated in 2005, 2006 and 2007 are considered for sample selection. The primary criterion of sample selection is availability of data for 10 years (starting from the inception); however, firms, with missing data of not more than two years, are also included. Continuous data are required to study the WCP of firms over their life cycle. The final sample consists of 162 firms, and data have been collected from Prowess database of the Centre for Monitoring Indian Economy (CMIE) for 2006–07 to 2015–16, 2007–08 to 2016–17, and 2008–09 to 2017–18, for firms incorporated in 2005, 2006 and 2007, respectively. Prowess database collects information on Indian companies from the annual reports, financial statements and stock exchanges and covers more than 40,000 companies including listed, unlisted and private companies.

3.3 Data analysis techniques

To test the hypotheses, the concept of β-convergence, and σ-convergence from the literature on economic growth (Young et al., 2008) has been adopted. β-convergence exists when regions with a lower initial GDP level tend to grow significantly faster than regions with higher initial GDP levels (Hirsch and Walz, 2019). Presently, β-convergence occurs when firms with lower levels of initial working capital investments tend to accumulate higher current assets as compared to those with higher initial working capital investment. Besides, to study dispersion in working capital ratios, σ-convergence has been adopted following Hirsch and Walz (2019).

Further, growth rate of each working capital ratio has been regressed to its initial year value (for TP and OCL, the value is taken for third year) to study the effect of initial WCP on subsequent year. As present study uses single-year cross-sectional data, standard least square method has been adopted as it assumes the data as a single pool. Further, effect of firm characteristics and industry link has been accounted by taking control variables and industry dummies. Lastly, generalized method of moment (GMM) estimation has been used to control potential endogeneity problem as it allows the use of large data on time structure of WCP dynamics. The regression equation is formed as follows:

(1)Growth (Y)i =c +β1 ×Yi0 +β2 ×Xi0 +β3 ×INDDUMi0 +ε

where,

• Growth (Y)_i = the growth in WCP ratios considered for the study

• Y_i0 = r the initial value of WCP ratios considered for the study

• X_i0 = the initial value of firm-specific control variables as sales to total assets (S/TA), fixed assets (FA), long-term debt (LTD), total assets (TA)

• INDDUM = Dummy variable taken as 1 if the company belongs to the given industry and 0 otherwise

• ε = Error term

3.4 Descriptive statistics

Table 2 (available online at: https://drive.google.com/drive/folders/1NpOVvfjKX2cTrMFWrr_Mz-Zozgh6tOLL?usp=sharing) summarizes the descriptive results of the sample company's characteristics. In the initial year after incorporation, firms rely more upon long-term debt as a source of funds, which gradually shifted to current liabilities by year 10 as mean value improved from 23% to 49%. Further, initially firms invested more in fixed assets (64%), which also shifted to current assets (47%) over a period of time. Results for control variable indicate that most firms are small during incorporation as can be analysed through the median value of the sales and total assets; however, higher mean values indicate the existence of few very large firms. Besides the average profitability (measured by RoA and profit margin) improving from −2.14% to −0.12%, the results show poor performance of firms.

4. Empirical results

4.1 Initial working capital policy

Table 3 (panel 3.1; available online at: https://drive.google.com/drive/folders/1NpOVvfjKX2cTrMFWrr_Mz-Zozgh6tOLL?usp=sharing) summarizes the descriptive statistics of selected working capital ratios for the sample companies on completion of 1 year from the date of incorporation (except for TP and OCL). Results indicate that average investment in current assets is 36% of the total, whereas current liabilities account for 23% of the total liabilities. Further, the mean value of current ratio is more than 3, indicating that firms follow conservative WCP. Besides, to study the industry-level differences, the mean value of working capital ratio has been compared using t-test (refer table 3: panel – 3.2), and the differences are found to be statistically significant. For instance, consumer durable firms invest 72% of their total assets in working capital, whereas construction material firms invest the least with only 12% of total assets. However, it is found commonly that firms, even though from different industries, maintain higher proportion of current assets as compared to current liabilities. Apart from industry, the mean ratios of working capital are also analysed for differences according to firm size (available table 3: panel 3.3), region and ownership (included in supplementary file – available online at: https://drive.google.com/drive/folders/1NpOVvfjKX2cTrMFWrr_Mz-Zozgh6tOLL), but the results are not significant which further indicate that initial WCP of selected firms is not affected by these factors.

4.2 Evolution of working capital policy

The present section reports how the WCP of the firms have evolved during the study period by grouping the firms according to their initial working capital ratios. Firms are categorized into four groups according to 25th, 50th and 75th percentile of each working capital ratio. Further, firms classified in a particular group have remained in the same group to analyse the changes in the selected ratios over the period of 10 years. The mean ratios of all selected ratios have been compared between the groups using t-test, and results are summarized in table 4 (available online at: https://drive.google.com/drive/folders/1NpOVvfjKX2cTrMFWrr_Mz-Zozgh6tOLL?usp=sharing) which are computed for each year; however, figures of year 1, year 5 and year 10 are reported for brevity purpose. Mean ratios of all variables are found to be significantly different (sign. value < 0.05) between the respective groups under comparison for all years. These results confirm the presence of β-convergence and lead to acceptance of H2. Further, firms' approach towards working capital finance (measured by CL) differs from working capital investment (measured by CA) as all WCFP ratios significantly differ (p-value <0.01) for year 1 only, whereas that of WCIP differ for constantly from year 1, year 5 and year 10 across the groups. Such results indicate that firms adopt varying adjustment speed for WCIP and WCFP.

4.3 Result of β-convergence using standard least squares regression

Table 5 (available online at: https://drive.google.com/drive/folders/1NpOVvfjKX2cTrMFWrr_Mz-Zozgh6tOLL?usp=sharing) portrays regression results of standard least square method and indicates that all models (except for TP and OCL) are statistically significant at least at 5% level. The results convey the presence of β-convergence as indicated by the negative regression coefficients. Negative coefficient can be explained as firms, with higher level initial working capital ratios, experience greater reduction in the same over the study period. However, in the case of CR, CA, CL and OCL, the regression coefficients are negative but lack statistical significance. The results are found significant for WCIP. Hence, the cross-sectional analysis confirms that the β-convergence is present as well as mean reversion in the WCP of newly incorporated firms.

4.3.1 Robustness check for multicollinearity and autocorrelation

Results obtained from multiple regression analysis are sensitive to multicollinearity and autocorrelation, and hence, the reliability of output needs to be assessed. Multicollinearity exists due to high correlation within independent variables, whereas autocorrelation occurs when the value of one variable at time t is related to the value of the same variable at the previous time. To control these anxieties, we have applied the variance inflation factor (VIF) and Durbin–Watson (DW) test. VIF values (<10) and DW statistics (1.5–2.5) (Table 5) are within the acceptable range (Gujarati, 2003), hence problem of multicollinearity and autocorrelation has been controlled.

4.3.2 Robustness – β-convergence using dynamic panel regression

In this step, dynamic panel data regression, using GMM estimate (Arellano and Bond, 1991), has been applied to check robustness of our previous result. The potential endogeneity issue arises due to the reverse causality between independent and dependent variables. In the present work, the growth in current asset ratio may affect the total assets of the firm, leading to potential issue of simultaneity. Further, WCP ratios might be explained by their own lagged variables as well as by firm-specific factors, both of which are non-exogenous. Hence, we have adopted a two-step system GMM against difference GMM (Laghari and Chengang, 2019) and have used the lagged independent variables as instruments to overcome the endogeneity problem. Further, we have run the test of autocorrelation along with the Sargan test for over-identifying restrictions (Moussa, 2019).

The regression coefficients of the first lag values of endogenous variables are significantly different from zero for all WCP ratios except CR, CA and CL shown in Table  6 (available online at: https://drive.google.com/drive/folders/1NpOVvfjKX2cTrMFWrr_Mz-Zozgh6tOLL?usp=sharing). Overall, it can be interpreted that complete persistence exists when the first lag of the independent variable has coefficient value (α) as one. Hence, the speed of convergence can be given by the expression (1 – α) (Hirsch and Walz, 2019); so OCL (α = 0.3139) reports the fastest convergence, whereas INT (α = 0.5612) converges very slowly. To check the effect of higher-order autoregressive processes on convergence, we have added the second lag of endogenous variables. The results indicate that the β-convergence process with two lags is relatively slower as compared to only one lag, as the positive coefficient of the second lag reduces the speed of convergence. Cumulatively, the results of dynamic panel data indicate the β-convergence and in congruence with our conclusion of cross-sectional regression. Thus, both hypotheses are strongly supported even by the result of panel analysis.

4.4 Result of Wald test for σ-convergence

As indicated earlier, the results confirm that the magnitude of mean difference in working capital ratios decreased during years 1–10 for all groups. However, the difference is still significant, indicating the effect of initial WCP. Therefore, σ-convergence has been implemented to check the change in variance over time. σ-convergence exists if the variance of the WCP ratio significantly declines over time, whereas σ–divergence concludes the reverse. Lichtenberg (1994) has proposed an F-test for the differences between initial variance and final variance, but its suppositions ignore the dependency between the variances. Hence, we have further applied an efficient Wald test (Egger and Pfaffermayr, 2009) for the difference between original and final variance which overcomes this concern.

Table 7 (available online at: https://drive.google.com/drive/folders/1NpOVvfjKX2cTrMFWrr_Mz-Zozgh6tOLL?usp=sharing) summarizes the initial and final variance of the WCP ratio, the Wald statistics and their interpretation. As the Wald statistic (W₀) is larger than 2.71, the difference between variance is statistically significant (Egger and Pfaffermayr, 2009). The results indicate σ-convergence in CR, CATA, CSTA and OLTL ratios (as the variance of the final year is lower than initial variance), whereas the rest have reported σ -divergence. Thus, we get mixed results for WCP ratios; the difference in some ratios has reduced, while the difference in other ratios has increased over the ten years. Therefore, both hypotheses are strongly supported even by the result of panel analysis.

5. Initial WCP and firm's growth

To examine the effect of initial WCP on firms' developmental trajectory, compounded annual growth in sales and total asset has been computed (Hirsch and Walz, 2019) (table 8; available online at: https://drive.google.com/drive/folders/1NpOVvfjKX2cTrMFWrr_Mz-Zozgh6tOLL?usp=sharing) as new firms concentrate more on achieving sustainability rather than short-term profitability. Firms in group 1 have reported the highest sales growth among all, which it tends to reduce gradually from firms for all ratios. Firms in G1 of current assets show the highest sales growth, and growth rate decreases gradually for G1 to G4 firms, whereas G4 firms have the highest total assets growth. In case of current liabilities, both G1 and G4 firms show more sales and asset growth compared to G2 and G3 firms, showing that either extreme conservative or extreme aggressive firms have seen the highest growth. Overall, it is concluded that firms that have adopted aggressive policy initially have found to have more progress compared to the firms that have adopted initial conservative policy, and this conclusion is in line with theoretical concept that high risk leads to higher returns (Bhalla, 2014).

6. Discussion and implications

The present study purports to examine the evolution of WCP over the life cycle of the newly incorporated manufacturing companies in India. Findings of t-test indicate that mean ratios of all variables are found to be significantly different (sign. value < 0.05), confirming the presence of β-convergence, that is, differences in initial working capital ratios persist over time. The results convey the presence of β-convergence as indicated by the negative regression coefficients. Negative coefficient can be explained as firms with higher level initial working capital ratios experience greater reduction in the same over the study period. It confirms the findings that firms which are initially operating at the extremes (either aggressive or conservative) move towards each other and narrow the gap. However, the mean ratios of group 1 company have increased for CA, CL and CR, whereas that of group 4 companies have decreased over the study period, indicating that firms which had adopted aggressive (conservative) WCP by keeping lower (higher) current assets in year 1 have gradually increased (decreased) their current assets over the years. Similar conclusion can be drawn for current liabilities as well wherein firms with lower current liabilities in the initial year have accumulated higher current liabilities by the end of study period. This can be explained as companies devise their initial WCP based on set of assumptions, forecasts and perceived industry scenarios, which later on change in response to environmental fluctuations as well as experience of the firm (Mittal and Garg, 2022). Findings are congruent with past results of Hirsch and Walz (2019) and Khan et al. (2022), and also confirm the hypothesis. However, though results conclude convergence of initial difference in working capital ratios; firms continue to follow their legacy of inception as the difference between the mean ratios of respective groups does not eliminate. It can be explained as characteristics of promoters and founders of the firm have long-lasting effects on corporate decision-making, and hence the conservative (aggressive) firms continue to pursue the same strategy over the period of time. As very limited studies have explored the dynamic nature of working capital, present findings ignite the research in this domain and provide a breakthrough for further exploration. Further, it has several important implications for academicians and practitioners, as explained in the subsequent sections.

6.1 Theoretical implications

The present study enumerates several important contributions to this growing area of working capital research as well as in the field of corporate finance. First, past studies have premeditated working capital as a static concept; however, to the best of the authors' knowledge, present research pioneered to analyse dynamics of working capital by employing two prominent concepts of economics domain, β-convergence and σ-convergence. Second, the study is based one of the fastest emerging nations of the Asian continent wherein such research is less explored. Third, through this research, a novel aspect of working capital research is brought by considering the approach of newly incorporated and young firms. Fourth, current research uses several measures to control statistical issues such as multicollinearity, auto-correlation as well as potential endogeneity. The findings are robust for all these concerns and are free from statistical biases.

6.2 Practical implications

Considering the dearth of research in the field of working capital dynamics, the present study has several implications for managers and practitioners. First, the findings indicate persistent-cum-convergence behaviour of working capital decision. Further, the difference in initial WCP, though converge, but does not eliminate. It suggests significant impact of initial WCP on the subsequent policies. Hence, though working capital decisions are operational in nature, managers should consider this long-term effect of WCP instead of considering working capital as a contingent decision only. Second, results indicate significant difference among the mean values of working capital ratios across various industries; therefore, nature of business should be considered as a significant determinant by practitioners while devising the WCP. Third, the findings indicate that inventories possess substantial share in the proportion of total current assets leading to suboptimal returns on investment. Hence, corporate practitioners should reduce their inventory levels possibly by applying any one or a combination of these techniques: reduction of supplier lead time, inventory classification using ABC analysis, shorter ordering cycle and so forth. In the long run, the company may plan to switch to real-time techniques such as “just-in-time” (JIT).

7. Conclusion and scope of future research

WCP of newly incorporated firms has been studied. For this, the data of Indian manufacturing firms that were incorporated between 2005 and 2007 have been taken. Based on thorough analysis of WCP over a period of ten years, there are three findings. First, in the initial year after incorporation, firms' WCP has a larger proportion of working capital investments than working capital finance, and the highest form of investment is in inventory. Firms do not have short-term borrowings and trade payables in the initial few years after incorporation. Second, the initial WCP has not changed drastically in the subsequent years; instead, there has been a gradual convergence in the initial WCP and the initial difference remains. This finding is based on the results of two convergence concepts: β-convergence and σ-convergence. Thus, convergence-cum-persistence WCP for the newly incorporated firms is found. Third, the firms with aggressive initial WCP have seen higher growth as compared to firms with conservative initial WCP.

7.1 Scope of future research

The present study attempts to provide a comprehensive analysis about the dynamic nature of working capital; however, certain areas can be further probed through further research. First, present study can be extended by considering the behavioural variables of managers as well as corporate governance variables such as ownership structure and CEO duality to explain the qualitative determinants of WCP. Second, a comparative study for developed and emerging economies can be done to analyse cross-country differences. Lastly, findings of present study may be affected by the time frame of data collection and need to be validated before applying to a different time horizon.

Note: Supplementary materials that are included in the article are available online.